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Explore the Titanic data on Kaggle
Raw
titanicAnalysis
{
"metadata": {
"language": "Julia",
"name": "",
"signature": "sha256:6394928c631f491d9b3776e1af071144d00c1446e0ec4d032c094af7b3d8edd6"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploratory Analyses of Titanic data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load packages and data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"using Gadfly\n",
"using DataFrames\n",
"df=readtable(\"train.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"data-frame\"><tr><th></th><th>PassengerId</th><th>Survived</th><th>Pclass</th><th>Name</th><th>Sex</th><th>Age</th><th>SibSp</th><th>Parch</th><th>Ticket</th><th>Fare</th><th>Cabin</th><th>Embarked</th></tr><tr><th>1</th><td>1</td><td>0</td><td>3</td><td>Braund, Mr. Owen Harris</td><td>male</td><td>22.0</td><td>1</td><td>0</td><td>A/5 21171</td><td>7.25</td><td>NA</td><td>S</td></tr><tr><th>2</th><td>2</td><td>1</td><td>1</td><td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td><td>female</td><td>38.0</td><td>1</td><td>0</td><td>PC 17599</td><td>71.2833</td><td>C85</td><td>C</td></tr><tr><th>3</th><td>3</td><td>1</td><td>3</td><td>Heikkinen, Miss. Laina</td><td>female</td><td>26.0</td><td>0</td><td>0</td><td>STON/O2. 3101282</td><td>7.925</td><td>NA</td><td>S</td></tr><tr><th>4</th><td>4</td><td>1</td><td>1</td><td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td><td>female</td><td>35.0</td><td>1</td><td>0</td><td>113803</td><td>53.1</td><td>C123</td><td>S</td></tr><tr><th>5</th><td>5</td><td>0</td><td>3</td><td>Allen, Mr. William Henry</td><td>male</td><td>35.0</td><td>0</td><td>0</td><td>373450</td><td>8.05</td><td>NA</td><td>S</td></tr><tr><th>6</th><td>6</td><td>0</td><td>3</td><td>Moran, Mr. James</td><td>male</td><td>NA</td><td>0</td><td>0</td><td>330877</td><td>8.4583</td><td>NA</td><td>Q</td></tr><tr><th>7</th><td>7</td><td>0</td><td>1</td><td>McCarthy, Mr. Timothy J</td><td>male</td><td>54.0</td><td>0</td><td>0</td><td>17463</td><td>51.8625</td><td>E46</td><td>S</td></tr><tr><th>8</th><td>8</td><td>0</td><td>3</td><td>Palsson, Master. Gosta Leonard</td><td>male</td><td>2.0</td><td>3</td><td>1</td><td>349909</td><td>21.075</td><td>NA</td><td>S</td></tr><tr><th>9</th><td>9</td><td>1</td><td>3</td><td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td><td>female</td><td>27.0</td><td>0</td><td>2</td><td>347742</td><td>11.1333</td><td>NA</td><td>S</td></tr><tr><th>10</th><td>10</td><td>1</td><td>2</td><td>Nasser, Mrs. Nicholas (Adele Achem)</td><td>female</td><td>14.0</td><td>1</td><td>0</td><td>237736</td><td>30.0708</td><td>NA</td><td>C</td></tr><tr><th>11</th><td>11</td><td>1</td><td>3</td><td>Sandstrom, Miss. Marguerite Rut</td><td>female</td><td>4.0</td><td>1</td><td>1</td><td>PP 9549</td><td>16.7</td><td>G6</td><td>S</td></tr><tr><th>12</th><td>12</td><td>1</td><td>1</td><td>Bonnell, Miss. Elizabeth</td><td>female</td><td>58.0</td><td>0</td><td>0</td><td>113783</td><td>26.55</td><td>C103</td><td>S</td></tr><tr><th>13</th><td>13</td><td>0</td><td>3</td><td>Saundercock, Mr. William Henry</td><td>male</td><td>20.0</td><td>0</td><td>0</td><td>A/5. 2151</td><td>8.05</td><td>NA</td><td>S</td></tr><tr><th>14</th><td>14</td><td>0</td><td>3</td><td>Andersson, Mr. Anders Johan</td><td>male</td><td>39.0</td><td>1</td><td>5</td><td>347082</td><td>31.275</td><td>NA</td><td>S</td></tr><tr><th>15</th><td>15</td><td>0</td><td>3</td><td>Vestrom, Miss. Hulda Amanda Adolfina</td><td>female</td><td>14.0</td><td>0</td><td>0</td><td>350406</td><td>7.8542</td><td>NA</td><td>S</td></tr><tr><th>16</th><td>16</td><td>1</td><td>2</td><td>Hewlett, Mrs. (Mary D Kingcome) </td><td>female</td><td>55.0</td><td>0</td><td>0</td><td>248706</td><td>16.0</td><td>NA</td><td>S</td></tr><tr><th>17</th><td>17</td><td>0</td><td>3</td><td>Rice, Master. Eugene</td><td>male</td><td>2.0</td><td>4</td><td>1</td><td>382652</td><td>29.125</td><td>NA</td><td>Q</td></tr><tr><th>18</th><td>18</td><td>1</td><td>2</td><td>Williams, Mr. Charles Eugene</td><td>male</td><td>NA</td><td>0</td><td>0</td><td>244373</td><td>13.0</td><td>NA</td><td>S</td></tr><tr><th>19</th><td>19</td><td>0</td><td>3</td><td>Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)</td><td>female</td><td>31.0</td><td>1</td><td>0</td><td>345763</td><td>18.0</td><td>NA</td><td>S</td></tr><tr><th>20</th><td>20</td><td>1</td><td>3</td><td>Masselmani, Mrs. Fatima</td><td>female</td><td>NA</td><td>0</td><td>0</td><td>2649</td><td>7.225</td><td>NA</td><td>C</td></tr><tr><th>21</th><td>21</td><td>0</td><td>2</td><td>Fynney, Mr. Joseph J</td><td>male</td><td>35.0</td><td>0</td><td>0</td><td>239865</td><td>26.0</td><td>NA</td><td>S</td></tr><tr><th>22</th><td>22</td><td>1</td><td>2</td><td>Beesley, Mr. Lawrence</td><td>male</td><td>34.0</td><td>0</td><td>0</td><td>248698</td><td>13.0</td><td>D56</td><td>S</td></tr><tr><th>23</th><td>23</td><td>1</td><td>3</td><td>McGowan, Miss. Anna \"Annie\"</td><td>female</td><td>15.0</td><td>0</td><td>0</td><td>330923</td><td>8.0292</td><td>NA</td><td>Q</td></tr><tr><th>24</th><td>24</td><td>1</td><td>1</td><td>Sloper, Mr. William Thompson</td><td>male</td><td>28.0</td><td>0</td><td>0</td><td>113788</td><td>35.5</td><td>A6</td><td>S</td></tr><tr><th>25</th><td>25</td><td>0</td><td>3</td><td>Palsson, Miss. Torborg Danira</td><td>female</td><td>8.0</td><td>3</td><td>1</td><td>349909</td><td>21.075</td><td>NA</td><td>S</td></tr><tr><th>26</th><td>26</td><td>1</td><td>3</td><td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)</td><td>female</td><td>38.0</td><td>1</td><td>5</td><td>347077</td><td>31.3875</td><td>NA</td><td>S</td></tr><tr><th>27</th><td>27</td><td>0</td><td>3</td><td>Emir, Mr. Farred Chehab</td><td>male</td><td>NA</td><td>0</td><td>0</td><td>2631</td><td>7.225</td><td>NA</td><td>C</td></tr><tr><th>28</th><td>28</td><td>0</td><td>1</td><td>Fortune, Mr. Charles Alexander</td><td>male</td><td>19.0</td><td>3</td><td>2</td><td>19950</td><td>263.0</td><td>C23 C25 C27</td><td>S</td></tr><tr><th>29</th><td>29</td><td>1</td><td>3</td><td>O'Dwyer, Miss. Ellen \"Nellie\"</td><td>female</td><td>NA</td><td>0</td><td>0</td><td>330959</td><td>7.8792</td><td>NA</td><td>Q</td></tr><tr><th>30</th><td>30</td><td>0</td><td>3</td><td>Todoroff, Mr. Lalio</td><td>male</td><td>NA</td><td>0</td><td>0</td><td>349216</td><td>7.8958</td><td>NA</td><td>S</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"891x12 DataFrame\n",
"| Row | PassengerId | Survived | Pclass |\n",
"|-----|-------------|----------|--------|\n",
"| 1 | 1 | 0 | 3 |\n",
"| 2 | 2 | 1 | 1 |\n",
"| 3 | 3 | 1 | 3 |\n",
"| 4 | 4 | 1 | 1 |\n",
"| 5 | 5 | 0 | 3 |\n",
"| 6 | 6 | 0 | 3 |\n",
"| 7 | 7 | 0 | 1 |\n",
"| 8 | 8 | 0 | 3 |\n",
"| 9 | 9 | 1 | 3 |\n",
"| 10 | 10 | 1 | 2 |\n",
"| 11 | 11 | 1 | 3 |\n",
"\u22ee\n",
"| 880 | 880 | 1 | 1 |\n",
"| 881 | 881 | 1 | 2 |\n",
"| 882 | 882 | 0 | 3 |\n",
"| 883 | 883 | 0 | 3 |\n",
"| 884 | 884 | 0 | 2 |\n",
"| 885 | 885 | 0 | 3 |\n",
"| 886 | 886 | 0 | 3 |\n",
"| 887 | 887 | 0 | 2 |\n",
"| 888 | 888 | 1 | 1 |\n",
"| 889 | 889 | 0 | 3 |\n",
"| 890 | 890 | 1 | 1 |\n",
"| 891 | 891 | 0 | 3 |\n",
"\n",
"| Row | Name | Sex |\n",
"|-----|-------------------------------------------------------|----------|\n",
"| 1 | \"Braund, Mr. Owen Harris\" | \"male\" |\n",
"| 2 | \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\" | \"female\" |\n",
"| 3 | \"Heikkinen, Miss. Laina\" | \"female\" |\n",
"| 4 | \"Futrelle, Mrs. Jacques Heath (Lily May Peel)\" | \"female\" |\n",
"| 5 | \"Allen, Mr. William Henry\" | \"male\" |\n",
"| 6 | \"Moran, Mr. James\" | \"male\" |\n",
"| 7 | \"McCarthy, Mr. Timothy J\" | \"male\" |\n",
"| 8 | \"Palsson, Master. Gosta Leonard\" | \"male\" |\n",
"| 9 | \"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\" | \"female\" |\n",
"| 10 | \"Nasser, Mrs. Nicholas (Adele Achem)\" | \"female\" |\n",
"| 11 | \"Sandstrom, Miss. Marguerite Rut\" | \"female\" |\n",
"\u22ee\n",
"| 880 | \"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)\" | \"female\" |\n",
"| 881 | \"Shelley, Mrs. William (Imanita Parrish Hall)\" | \"female\" |\n",
"| 882 | \"Markun, Mr. Johann\" | \"male\" |\n",
"| 883 | \"Dahlberg, Miss. Gerda Ulrika\" | \"female\" |\n",
"| 884 | \"Banfield, Mr. Frederick James\" | \"male\" |\n",
"| 885 | \"Sutehall, Mr. Henry Jr\" | \"male\" |\n",
"| 886 | \"Rice, Mrs. William (Margaret Norton)\" | \"female\" |\n",
"| 887 | \"Montvila, Rev. Juozas\" | \"male\" |\n",
"| 888 | \"Graham, Miss. Margaret Edith\" | \"female\" |\n",
"| 889 | \"Johnston, Miss. Catherine Helen \\\"Carrie\\\"\" | \"female\" |\n",
"| 890 | \"Behr, Mr. Karl Howell\" | \"male\" |\n",
"| 891 | \"Dooley, Mr. Patrick\" | \"male\" |\n",
"\n",
"| Row | Age | SibSp | Parch | Ticket | Fare | Cabin |\n",
"|-----|------|-------|-------|--------------------|---------|--------|\n",
"| 1 | 22.0 | 1 | 0 | \"A/5 21171\" | 7.25 | NA |\n",
"| 2 | 38.0 | 1 | 0 | \"PC 17599\" | 71.2833 | \"C85\" |\n",
"| 3 | 26.0 | 0 | 0 | \"STON/O2. 3101282\" | 7.925 | NA |\n",
"| 4 | 35.0 | 1 | 0 | \"113803\" | 53.1 | \"C123\" |\n",
"| 5 | 35.0 | 0 | 0 | \"373450\" | 8.05 | NA |\n",
"| 6 | NA | 0 | 0 | \"330877\" | 8.4583 | NA |\n",
"| 7 | 54.0 | 0 | 0 | \"17463\" | 51.8625 | \"E46\" |\n",
"| 8 | 2.0 | 3 | 1 | \"349909\" | 21.075 | NA |\n",
"| 9 | 27.0 | 0 | 2 | \"347742\" | 11.1333 | NA |\n",
"| 10 | 14.0 | 1 | 0 | \"237736\" | 30.0708 | NA |\n",
"| 11 | 4.0 | 1 | 1 | \"PP 9549\" | 16.7 | \"G6\" |\n",
"\u22ee\n",
"| 880 | 56.0 | 0 | 1 | \"11767\" | 83.1583 | \"C50\" |\n",
"| 881 | 25.0 | 0 | 1 | \"230433\" | 26.0 | NA |\n",
"| 882 | 33.0 | 0 | 0 | \"349257\" | 7.8958 | NA |\n",
"| 883 | 22.0 | 0 | 0 | \"7552\" | 10.5167 | NA |\n",
"| 884 | 28.0 | 0 | 0 | \"C.A./SOTON 34068\" | 10.5 | NA |\n",
"| 885 | 25.0 | 0 | 0 | \"SOTON/OQ 392076\" | 7.05 | NA |\n",
"| 886 | 39.0 | 0 | 5 | \"382652\" | 29.125 | NA |\n",
"| 887 | 27.0 | 0 | 0 | \"211536\" | 13.0 | NA |\n",
"| 888 | 19.0 | 0 | 0 | \"112053\" | 30.0 | \"B42\" |\n",
"| 889 | NA | 1 | 2 | \"W./C. 6607\" | 23.45 | NA |\n",
"| 890 | 26.0 | 0 | 0 | \"111369\" | 30.0 | \"C148\" |\n",
"| 891 | 32.0 | 0 | 0 | \"370376\" | 7.75 | NA |\n",
"\n",
"| Row | Embarked |\n",
"|-----|----------|\n",
"| 1 | \"S\" |\n",
"| 2 | \"C\" |\n",
"| 3 | \"S\" |\n",
"| 4 | \"S\" |\n",
"| 5 | \"S\" |\n",
"| 6 | \"Q\" |\n",
"| 7 | \"S\" |\n",
"| 8 | \"S\" |\n",
"| 9 | \"S\" |\n",
"| 10 | \"C\" |\n",
"| 11 | \"S\" |\n",
"\u22ee\n",
"| 880 | \"C\" |\n",
"| 881 | \"S\" |\n",
"| 882 | \"S\" |\n",
"| 883 | \"S\" |\n",
"| 884 | \"S\" |\n",
"| 885 | \"S\" |\n",
"| 886 | \"Q\" |\n",
"| 887 | \"S\" |\n",
"| 888 | \"S\" |\n",
"| 889 | \"S\" |\n",
"| 890 | \"C\" |\n",
"| 891 | \"Q\" |"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List the summary of the data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"describe(df)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"PassengerId\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Min 1.0"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"1st Qu. 223.5\n",
"Median 446.0\n",
"Mean 446.0\n",
"3rd Qu. 668.5\n",
"Max 891.0\n",
"NAs 0\n",
"NA% 0.0%\n",
"\n",
"Survived\n",
"Min 0.0\n",
"1st Qu. 0.0\n",
"Median 0.0\n",
"Mean 0.3838383838383838\n",
"3rd Qu. 1.0\n",
"Max 1.0\n",
"NAs 0\n",
"NA% 0.0%\n",
"\n",
"Pclass\n",
"Min 1.0\n",
"1st Qu. 2.0\n",
"Median 3.0\n",
"Mean 2.308641975308642\n",
"3rd Qu. 3.0\n",
"Max 3.0\n",
"NAs 0\n",
"NA% 0.0%\n",
"\n",
"Name\n",
"Length 891\n",
"Type UTF8String\n",
"NAs 0\n",
"NA% 0.0%\n",
"Unique 891\n",
"\n",
"Sex\n",
"Length 891\n",
"Type UTF8String\n",
"NAs 0\n",
"NA% 0.0%\n",
"Unique 2\n",
"\n",
"Age\n",
"Min 0.42\n",
"1st Qu. 20.125\n",
"Median 28.0\n",
"Mean 29.69911764705882\n",
"3rd Qu. 38.0\n",
"Max 80.0\n",
"NAs 177\n",
"NA% 19.87%\n",
"\n",
"SibSp\n",
"Min 0.0\n",
"1st Qu. 0.0\n",
"Median 0.0\n",
"Mean 0.5230078563411896\n",
"3rd Qu. 1.0\n",
"Max 8.0\n",
"NAs 0\n",
"NA% 0.0%\n",
"\n",
"Parch\n",
"Min 0.0\n",
"1st Qu. 0.0\n",
"Median 0.0\n",
"Mean 0.38159371492704824\n",
"3rd Qu. 0.0\n",
"Max 6.0\n",
"NAs 0\n",
"NA% 0.0%\n",
"\n",
"Ticket\n",
"Length 891\n",
"Type UTF8String\n",
"NAs 0\n",
"NA% 0.0%\n",
"Unique 681\n",
"\n",
"Fare\n",
"Min 0.0\n",
"1st Qu. 7.9104\n",
"Median 14.4542\n",
"Mean 32.20420796857464\n",
"3rd Qu. 31.0\n",
"Max 512.3292\n",
"NAs 0\n",
"NA% 0.0%\n",
"\n",
"Cabin\n",
"Length 891\n",
"Type UTF8String\n",
"NAs 687\n",
"NA% 77.1%\n",
"Unique 148\n",
"\n",
"Embarked\n",
"Length 891\n",
"Type UTF8String\n",
"NAs 2\n",
"NA% 0.22%\n",
"Unique 4\n",
"\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test the data type"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"typeof(df)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"DataFrame (constructor with 22 methods)"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the first row"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[1,:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"data-frame\"><tr><th></th><th>PassengerId</th><th>Survived</th><th>Pclass</th><th>Name</th><th>Sex</th><th>Age</th><th>SibSp</th><th>Parch</th><th>Ticket</th><th>Fare</th><th>Cabin</th><th>Embarked</th></tr><tr><th>1</th><td>1</td><td>0</td><td>3</td><td>Braund, Mr. Owen Harris</td><td>male</td><td>22.0</td><td>1</td><td>0</td><td>A/5 21171</td><td>7.25</td><td>NA</td><td>S</td></tr></table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"1x12 DataFrame\n",
"| Row | PassengerId | Survived | Pclass | Name | Sex |\n",
"|-----|-------------|----------|--------|---------------------------|--------|\n",
"| 1 | 1 | 0 | 3 | \"Braund, Mr. Owen Harris\" | \"male\" |\n",
"\n",
"| Row | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |\n",
"|-----|------|-------|-------|-------------|------|-------|----------|\n",
"| 1 | 22.0 | 1 | 0 | \"A/5 21171\" | 7.25 | NA | \"S\" |"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read the column of Passenger's name"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[:Name]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"891-element DataArray{UTF8String,1}:\n",
" \"Braund, Mr. Owen Harris\" \n",
" \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\"\n",
" \"Heikkinen, Miss. Laina\" \n",
" \"Futrelle, Mrs. Jacques Heath (Lily May Peel)\" \n",
" \"Allen, Mr. William Henry\" \n",
" \"Moran, Mr. James\" \n",
" \"McCarthy, Mr. Timothy J\" \n",
" \"Palsson, Master. Gosta Leonard\" \n",
" \"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\" \n",
" \"Nasser, Mrs. Nicholas (Adele Achem)\" \n",
" \"Sandstrom, Miss. Marguerite Rut\" \n",
" \"Bonnell, Miss. Elizabeth\" \n",
" \"Saundercock, Mr. William Henry\" \n",
" \u22ee \n",
" \"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)\" \n",
" \"Shelley, Mrs. William (Imanita Parrish Hall)\" \n",
" \"Markun, Mr. Johann\" \n",
" \"Dahlberg, Miss. Gerda Ulrika\" \n",
" \"Banfield, Mr. Frederick James\" \n",
" \"Sutehall, Mr. Henry Jr\" \n",
" \"Rice, Mrs. William (Margaret Norton)\" \n",
" \"Montvila, Rev. Juozas\" \n",
" \"Graham, Miss. Margaret Edith\" \n",
" \"Johnston, Miss. Catherine Helen \\\"Carrie\\\"\" \n",
" \"Behr, Mr. Karl Howell\" \n",
" \"Dooley, Mr. Patrick\" "
]
}
],
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},
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"pool!(df,[:Sex])\n",
"pool!(df,[:Survived])\n",
"pool!(df,[:Pclass])"
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{
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"plot(df,x=\"Sex\",color=\"Survived\",Geom.histogram)"
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" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
" element.transform(unscale_t);\n",
"\n",
" var y = cy * scale + ty;\n",
" element.attr(\"visibility\",\n",
" bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (xscalable) {\n",
" var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
" var xtrans = new Snap.Matrix().translate(tx, 0);\n",
" root.selectAll(\".yfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(yfixed_t);\n",
" });\n",
"\n",
" root.select(\".xlabels\")\n",
" .transform(yfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
"\n",
" element.transform(unscale_t);\n",
"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
" bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
" var size_attribs = [\"font-size\"];\n",
" var unscaled_selection = \".geometry, .geometry *\";\n",
" if (xscalable) {\n",
" size_attribs.push(\"rx\");\n",
" unscaled_selection += \", .xgridlines\";\n",
" }\n",
" if (yscalable) {\n",
" size_attribs.push(\"ry\");\n",
" unscaled_selection += \", .ygridlines\";\n",
" }\n",
"\n",
" root.selectAll(unscaled_selection)\n",
" .forEach(function (element, i) {\n",
" // circle need special help\n",
" if (element.node.nodeName == \"circle\") {\n",
" var cx = element.attribute(\"cx\"),\n",
" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
" return;\n",
" }\n",
"\n",
" for (i in size_attribs) {\n",
" var key = size_attribs[i];\n",
" var val = parseFloat(element.attribute(key));\n",
" if (val !== undefined && val != 0 && !isNaN(val)) {\n",
" element.attribute(key, val * old_scale / scale);\n",
" }\n",
" }\n",
" });\n",
"};\n",
"\n",
"\n",
"// Find the most appropriate tick scale and update label visibility.\n",
"var update_tickscale = function(root, scale, axis) {\n",
" if (!root.hasClass(axis + \"scalable\")) return;\n",
"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
" var best_tickscale = 1.0;\n",
" var best_tickscale_dist = Infinity;\n",
" for (tickscale in tickscales) {\n",
" var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
" if (dist < best_tickscale_dist) {\n",
" best_tickscale_dist = dist;\n",
" best_tickscale = tickscale;\n",
" }\n",
" }\n",
"\n",
" if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
" root.data(axis + \"tickscale\", best_tickscale);\n",
" var mark_inscale_gridlines = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" var mark_inscale_labels = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
" root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
" }\n",
"};\n",
"\n",
"\n",
"var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" // compute the viewport derived from tx, ty, and scale\n",
" var x_min = -width * scale - (scale * width - width),\n",
" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
" y_max = height * scale;\n",
"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
" if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
" if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
" if (root.data(\"xtickscales\") === undefined) {\n",
"\n",
" // index all the tick scales that are listed\n",
" var xtickscales = {};\n",
" var ytickscales = {};\n",
" var add_x_tick_scales = function (element, i) {\n",
" xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
" var add_y_tick_scales = function (element, i) {\n",
" ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
"\n",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
" root.select(\".ylabels\").selectAll(\"text\").forEach(add_y_tick_scales);\n",
"\n",
" root.data(\"xtickscales\", xtickscales);\n",
" root.data(\"ytickscales\", ytickscales);\n",
" root.data(\"xtickscale\", 1.0);\n",
" }\n",
"\n",
" var min_scale = 1.0, max_scale = 1.0;\n",
" for (scale in xtickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" for (scale in ytickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" root.data(\"min_scale\", min_scale);\n",
" root.data(\"max_scale\", max_scale);\n",
"\n",
" // store the original positions of labels\n",
" root.select(\".xlabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\n",
" });\n",
"\n",
" // mark grid lines and ticks as in or out of scale.\n",
" var mark_inscale = function (element, i) {\n",
" element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
" };\n",
"\n",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
" root.select(\".ylabels\").selectAll(\"text\").forEach(mark_inscale);\n",
"\n",
" // figure out the upper ond lower bounds on panning using the maximum\n",
" // and minum grid lines\n",
" var bounds = root.plotbounds();\n",
" var pan_bounds = {\n",
" x0: 0.0,\n",
" y0: 0.0,\n",
" x1: 0.0,\n",
" y1: 0.0\n",
" };\n",
"\n",
" root.select(\".xgridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
" pan_bounds.x0 = bounds.x1 - bbox.x;\n",
" }\n",
" if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
" pan_bounds.x1 = bounds.x0 - bbox.x;\n",
" }\n",
" }\n",
" });\n",
"\n",
" root.select(\".ygridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
" pan_bounds.y0 = bounds.y1 - bbox.y;\n",
" }\n",
" if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
" pan_bounds.y1 = bounds.y0 - bbox.y;\n",
" }\n",
" }\n",
" });\n",
"\n",
" // nudge these values a little\n",
" pan_bounds.x0 -= 5;\n",
" pan_bounds.x1 += 5;\n",
" pan_bounds.y0 -= 5;\n",
" pan_bounds.y1 += 5;\n",
" root.data(\"pan_bounds\", pan_bounds);\n",
"\n",
" // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
" // will be clipped.\n",
" root.select(\".xgridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.select(\".ygridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// Panning\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var dx0 = root.data(\"dx\"),\n",
" dy0 = root.data(\"dy\");\n",
"\n",
" root.data(\"dx\", dx);\n",
" root.data(\"dy\", dy);\n",
"\n",
" dx = dx - dx0;\n",
" dy = dy - dy0;\n",
"\n",
" var tx = tx0 + dx,\n",
" ty = ty0 + dy;\n",
"\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" init_pan_zoom(root);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
" new_scale = Math.max(\n",
" root.data(\"min_scale\"),\n",
" Math.min(root.data(\"max_scale\"), new_scale))\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseover = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseout = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomout_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var min_scale = root.data(\"min_scale\"),\n",
" scale = root.data(\"scale\");\n",
" Snap.animate(\n",
" scale,\n",
" Math.max(min_scale, scale / 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var max_scale = root.data(\"max_scale\"),\n",
" scale = root.data(\"scale\");\n",
"\n",
" Snap.animate(\n",
" scale,\n",
" Math.min(max_scale, scale * 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" if (scale >= 1.0) {\n",
" return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
" }\n",
" else {\n",
" return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n",
" }\n",
"}\n",
"\n",
"\n",
"var update_plot_scale = function(root, new_scale) {\n",
" var trans = scale_centered_translation(root, new_scale);\n",
" set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
"\n",
" root.selectAll(\".zoomslider_thumb\")\n",
" .forEach(function (element, i) {\n",
" var min_pos = element.data(\"min_pos\"),\n",
" max_pos = element.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
" element.transform(new Snap.Matrix().translate(\n",
" Math.max(min_pos, Math.min(\n",
" max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
" });\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n",
" var root = this.plotroot();\n",
" var min_pos = this.data(\"min_pos\"),\n",
" max_pos = this.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"old_scale\");\n",
"\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
" dx / (max_pos - min_pos);\n",
"\n",
" // compute the new scale\n",
" var new_scale;\n",
" if (xpos >= 0.5) {\n",
" new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
" }\n",
" else {\n",
" new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
" Math.log(min_scale));\n",
" }\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
"};\n",
"\n",
"\n",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
" require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
" factory(Snap, Gadfly);\n",
" });\n",
" } else {\n",
" factory(glob.Snap, glob.Gadfly);\n",
" }\n",
"})(window, function (Snap, Gadfly) {\n",
" var fig = Snap(\"#fig-8be528a403df4859ab87da7e32cf1464\");\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-9\")\n",
" .mouseenter(Gadfly.plot_mouseover)\n",
".mouseleave(Gadfly.plot_mouseout)\n",
".mousewheel(Gadfly.guide_background_scroll)\n",
".drag(Gadfly.guide_background_drag_onmove,\n",
" Gadfly.guide_background_drag_onstart,\n",
" Gadfly.guide_background_drag_onend)\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-12\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-12\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-13\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-13\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-18\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-18\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-18\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-20\")\n",
" .data(\"max_pos\", 106.11)\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-20\")\n",
" .data(\"min_pos\", 89.11)\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-20\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-21\")\n",
" .data(\"max_pos\", 106.11)\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-21\")\n",
" .data(\"min_pos\", 89.11)\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-21\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-21\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-21\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-22\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-22\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-8be528a403df4859ab87da7e32cf1464-element-22\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"metadata": {},
"output_type": "pyout",
"png": 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"| Row | PassengerId | Survived | Pclass |\n",
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"| 1 | 1 | 0 | 3 |\n",
"| 2 | 2 | 1 | 1 |\n",
"| 3 | 3 | 1 | 3 |\n",
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"| 704 | 880 | 1 | 1 |\n",
"| 705 | 881 | 1 | 2 |\n",
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"| 707 | 883 | 0 | 3 |\n",
"| 708 | 884 | 0 | 2 |\n",
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"| 710 | 886 | 0 | 3 |\n",
"| 711 | 887 | 0 | 2 |\n",
"| 712 | 888 | 1 | 1 |\n",
"| 713 | 890 | 1 | 1 |\n",
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"| 1 | \"Braund, Mr. Owen Harris\" | \"male\" |\n",
"| 2 | \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\" | \"female\" |\n",
"| 3 | \"Heikkinen, Miss. Laina\" | \"female\" |\n",
"| 4 | \"Futrelle, Mrs. Jacques Heath (Lily May Peel)\" | \"female\" |\n",
"| 5 | \"Allen, Mr. William Henry\" | \"male\" |\n",
"| 6 | \"McCarthy, Mr. Timothy J\" | \"male\" |\n",
"| 7 | \"Palsson, Master. Gosta Leonard\" | \"male\" |\n",
"| 8 | \"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\" | \"female\" |\n",
"| 9 | \"Nasser, Mrs. Nicholas (Adele Achem)\" | \"female\" |\n",
"| 10 | \"Sandstrom, Miss. Marguerite Rut\" | \"female\" |\n",
"| 11 | \"Bonnell, Miss. Elizabeth\" | \"female\" |\n",
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"| 705 | \"Shelley, Mrs. William (Imanita Parrish Hall)\" | \"female\" |\n",
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"| 707 | \"Dahlberg, Miss. Gerda Ulrika\" | \"female\" |\n",
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"| 713 | \"Behr, Mr. Karl Howell\" | \"male\" |\n",
"| 714 | \"Dooley, Mr. Patrick\" | \"male\" |\n",
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"| Row | Age | SibSp | Parch | Ticket | Fare | Cabin |\n",
"|-----|------|-------|-------|--------------------|---------|--------|\n",
"| 1 | 22.0 | 1 | 0 | \"A/5 21171\" | 7.25 | NA |\n",
"| 2 | 38.0 | 1 | 0 | \"PC 17599\" | 71.2833 | \"C85\" |\n",
"| 3 | 26.0 | 0 | 0 | \"STON/O2. 3101282\" | 7.925 | NA |\n",
"| 4 | 35.0 | 1 | 0 | \"113803\" | 53.1 | \"C123\" |\n",
"| 5 | 35.0 | 0 | 0 | \"373450\" | 8.05 | NA |\n",
"| 6 | 54.0 | 0 | 0 | \"17463\" | 51.8625 | \"E46\" |\n",
"| 7 | 2.0 | 3 | 1 | \"349909\" | 21.075 | NA |\n",
"| 8 | 27.0 | 0 | 2 | \"347742\" | 11.1333 | NA |\n",
"| 9 | 14.0 | 1 | 0 | \"237736\" | 30.0708 | NA |\n",
"| 10 | 4.0 | 1 | 1 | \"PP 9549\" | 16.7 | \"G6\" |\n",
"| 11 | 58.0 | 0 | 0 | \"113783\" | 26.55 | \"C103\" |\n",
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"| 704 | 56.0 | 0 | 1 | \"11767\" | 83.1583 | \"C50\" |\n",
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"| 706 | 33.0 | 0 | 0 | \"349257\" | 7.8958 | NA |\n",
"| 707 | 22.0 | 0 | 0 | \"7552\" | 10.5167 | NA |\n",
"| 708 | 28.0 | 0 | 0 | \"C.A./SOTON 34068\" | 10.5 | NA |\n",
"| 709 | 25.0 | 0 | 0 | \"SOTON/OQ 392076\" | 7.05 | NA |\n",
"| 710 | 39.0 | 0 | 5 | \"382652\" | 29.125 | NA |\n",
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"| 713 | 26.0 | 0 | 0 | \"111369\" | 30.0 | \"C148\" |\n",
"| 714 | 32.0 | 0 | 0 | \"370376\" | 7.75 | NA |\n",
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" this.node.addEventListener(\n",
" /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
" fn2);\n",
"\n",
" return this;\n",
" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
" // This is a function to directly update element attributes without going\n",
" // through eve.\n",
" Element.prototype.attribute = function(key, val) {\n",
" if (val === undefined) {\n",
" return this.node.getAttribute(key, val);\n",
" } else {\n",
" return this.node.setAttribute(key, val);\n",
" }\n",
" };\n",
"});\n",
"\n",
"\n",
"// When the plot is moused over, emphasize the grid lines.\n",
"Gadfly.plot_mouseover = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" xgridlines.data(\"unfocused_strokedash\",\n",
" xgridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
" ygridlines.data(\"unfocused_strokedash\",\n",
" ygridlines.attr(\"stroke-dasharray\").replace(/px/g, \"mm\"))\n",
"\n",
" // emphasize grid lines\n",
" var destcolor = root.data(\"focused_xgrid_color\");\n",
" xgridlines.attr(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"focused_ygrid_color\");\n",
" ygridlines.attr(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // reveal zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 1.0}, 250);\n",
"};\n",
"\n",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" var destcolor = root.data(\"unfocused_xgrid_color\");\n",
"\n",
" xgridlines.attr(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
" ygridlines.attr(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // hide zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 0.0}, 250);\n",
"};\n",
"\n",
"\n",
"var set_geometry_transform = function(root, tx, ty, scale) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
"\n",
" var old_scale = root.data(\"scale\");\n",
"\n",
" var xscale = xscalable ? scale : 1.0,\n",
" yscale = yscalable ? scale : 1.0;\n",
"\n",
" tx = xscalable ? tx : 0.0;\n",
" ty = yscalable ? ty : 0.0;\n",
"\n",
" var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
"\n",
" root.selectAll(\".geometry, image\")\n",
" .forEach(function (element, i) {\n",
" element.transform(t);\n",
" });\n",
"\n",
" bounds = root.plotbounds();\n",
"\n",
" if (yscalable) {\n",
" var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
" root.selectAll(\".xfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(xfixed_t);\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .transform(xfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
" element.transform(unscale_t);\n",
"\n",
" var y = cy * scale + ty;\n",
" element.attr(\"visibility\",\n",
" bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (xscalable) {\n",
" var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
" var xtrans = new Snap.Matrix().translate(tx, 0);\n",
" root.selectAll(\".yfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(yfixed_t);\n",
" });\n",
"\n",
" root.select(\".xlabels\")\n",
" .transform(yfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
"\n",
" element.transform(unscale_t);\n",
"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
" bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
" var size_attribs = [\"font-size\"];\n",
" var unscaled_selection = \".geometry, .geometry *\";\n",
" if (xscalable) {\n",
" size_attribs.push(\"rx\");\n",
" unscaled_selection += \", .xgridlines\";\n",
" }\n",
" if (yscalable) {\n",
" size_attribs.push(\"ry\");\n",
" unscaled_selection += \", .ygridlines\";\n",
" }\n",
"\n",
" root.selectAll(unscaled_selection)\n",
" .forEach(function (element, i) {\n",
" // circle need special help\n",
" if (element.node.nodeName == \"circle\") {\n",
" var cx = element.attribute(\"cx\"),\n",
" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
" return;\n",
" }\n",
"\n",
" for (i in size_attribs) {\n",
" var key = size_attribs[i];\n",
" var val = parseFloat(element.attribute(key));\n",
" if (val !== undefined && val != 0 && !isNaN(val)) {\n",
" element.attribute(key, val * old_scale / scale);\n",
" }\n",
" }\n",
" });\n",
"};\n",
"\n",
"\n",
"// Find the most appropriate tick scale and update label visibility.\n",
"var update_tickscale = function(root, scale, axis) {\n",
" if (!root.hasClass(axis + \"scalable\")) return;\n",
"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
" var best_tickscale = 1.0;\n",
" var best_tickscale_dist = Infinity;\n",
" for (tickscale in tickscales) {\n",
" var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
" if (dist < best_tickscale_dist) {\n",
" best_tickscale_dist = dist;\n",
" best_tickscale = tickscale;\n",
" }\n",
" }\n",
"\n",
" if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
" root.data(axis + \"tickscale\", best_tickscale);\n",
" var mark_inscale_gridlines = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" var mark_inscale_labels = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
" root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
" }\n",
"};\n",
"\n",
"\n",
"var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" // compute the viewport derived from tx, ty, and scale\n",
" var x_min = -width * scale - (scale * width - width),\n",
" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
" y_max = height * scale;\n",
"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
" if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
" if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
" if (root.data(\"xtickscales\") === undefined) {\n",
"\n",
" // index all the tick scales that are listed\n",
" var xtickscales = {};\n",
" var ytickscales = {};\n",
" var add_x_tick_scales = function (element, i) {\n",
" xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
" var add_y_tick_scales = function (element, i) {\n",
" ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
"\n",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n",
" root.select(\".ylabels\").selectAll(\"text\").forEach(add_y_tick_scales);\n",
"\n",
" root.data(\"xtickscales\", xtickscales);\n",
" root.data(\"ytickscales\", ytickscales);\n",
" root.data(\"xtickscale\", 1.0);\n",
" }\n",
"\n",
" var min_scale = 1.0, max_scale = 1.0;\n",
" for (scale in xtickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" for (scale in ytickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" root.data(\"min_scale\", min_scale);\n",
" root.data(\"max_scale\", max_scale);\n",
"\n",
" // store the original positions of labels\n",
" root.select(\".xlabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\n",
" });\n",
"\n",
" // mark grid lines and ticks as in or out of scale.\n",
" var mark_inscale = function (element, i) {\n",
" element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
" };\n",
"\n",
" root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n",
" root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n",
" root.select(\".ylabels\").selectAll(\"text\").forEach(mark_inscale);\n",
"\n",
" // figure out the upper ond lower bounds on panning using the maximum\n",
" // and minum grid lines\n",
" var bounds = root.plotbounds();\n",
" var pan_bounds = {\n",
" x0: 0.0,\n",
" y0: 0.0,\n",
" x1: 0.0,\n",
" y1: 0.0\n",
" };\n",
"\n",
" root.select(\".xgridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
" pan_bounds.x0 = bounds.x1 - bbox.x;\n",
" }\n",
" if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
" pan_bounds.x1 = bounds.x0 - bbox.x;\n",
" }\n",
" }\n",
" });\n",
"\n",
" root.select(\".ygridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
" pan_bounds.y0 = bounds.y1 - bbox.y;\n",
" }\n",
" if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
" pan_bounds.y1 = bounds.y0 - bbox.y;\n",
" }\n",
" }\n",
" });\n",
"\n",
" // nudge these values a little\n",
" pan_bounds.x0 -= 5;\n",
" pan_bounds.x1 += 5;\n",
" pan_bounds.y0 -= 5;\n",
" pan_bounds.y1 += 5;\n",
" root.data(\"pan_bounds\", pan_bounds);\n",
"\n",
" // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n",
" // will be clipped.\n",
" root.select(\".xgridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.select(\".ygridlines\")\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// Panning\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var dx0 = root.data(\"dx\"),\n",
" dy0 = root.data(\"dy\");\n",
"\n",
" root.data(\"dx\", dx);\n",
" root.data(\"dy\", dy);\n",
"\n",
" dx = dx - dx0;\n",
" dy = dy - dy0;\n",
"\n",
" var tx = tx0 + dx,\n",
" ty = ty0 + dy;\n",
"\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" init_pan_zoom(root);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
" new_scale = Math.max(\n",
" root.data(\"min_scale\"),\n",
" Math.min(root.data(\"max_scale\"), new_scale))\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseover = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseout = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomout_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var min_scale = root.data(\"min_scale\"),\n",
" scale = root.data(\"scale\");\n",
" Snap.animate(\n",
" scale,\n",
" Math.max(min_scale, scale / 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
" var max_scale = root.data(\"max_scale\"),\n",
" scale = root.data(\"scale\");\n",
"\n",
" Snap.animate(\n",
" scale,\n",
" Math.min(max_scale, scale * 1.5),\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" if (scale >= 1.0) {\n",
" return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
" }\n",
" else {\n",
" return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n",
" }\n",
"}\n",
"\n",
"\n",
"var update_plot_scale = function(root, new_scale) {\n",
" var trans = scale_centered_translation(root, new_scale);\n",
" set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
"\n",
" root.selectAll(\".zoomslider_thumb\")\n",
" .forEach(function (element, i) {\n",
" var min_pos = element.data(\"min_pos\"),\n",
" max_pos = element.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
" element.transform(new Snap.Matrix().translate(\n",
" Math.max(min_pos, Math.min(\n",
" max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
" });\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n",
" var root = this.plotroot();\n",
" var min_pos = this.data(\"min_pos\"),\n",
" max_pos = this.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"old_scale\");\n",
"\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
" dx / (max_pos - min_pos);\n",
"\n",
" // compute the new scale\n",
" var new_scale;\n",
" if (xpos >= 0.5) {\n",
" new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
" }\n",
" else {\n",
" new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
" Math.log(min_scale));\n",
" }\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(event) {\n",
" var root = this.plotroot();\n",
" init_pan_zoom(root);\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
"};\n",
"\n",
"\n",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
" require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
" factory(Snap, Gadfly);\n",
" });\n",
" } else {\n",
" factory(glob.Snap, glob.Gadfly);\n",
" }\n",
"})(window, function (Snap, Gadfly) {\n",
" var fig = Snap(\"#fig-6554c859282e4cab9ae9a878f25500dd\");\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-4\")\n",
" .mouseenter(Gadfly.plot_mouseover)\n",
".mouseleave(Gadfly.plot_mouseout)\n",
".mousewheel(Gadfly.guide_background_scroll)\n",
".drag(Gadfly.guide_background_drag_onmove,\n",
" Gadfly.guide_background_drag_onstart,\n",
" Gadfly.guide_background_drag_onend)\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-13\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-13\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-13\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-15\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-15\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-15\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-16\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-16\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-16\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-16\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-16\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-17\")\n",
" .data(\"mouseover_color\", \"#cd5c5c\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-17\")\n",
" .data(\"mouseout_color\", \"#6a6a6a\")\n",
";\n",
"fig.select(\"#fig-6554c859282e4cab9ae9a878f25500dd-element-17\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"metadata": {},
"output_type": "pyout",
"png": 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{
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"collapsed": false,
"input": [
"yTrain=array(yTrain)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"891-element Array{Int64,1}:\n",
" 0\n",
" 1\n",
" 1\n",
" 1\n",
" 0\n",
" 0\n",
" 0\n",
" 0\n",
" 1\n",
" 1\n",
" 1\n",
" 1\n",
" 0\n",
" \u22ee\n",
" 1\n",
" 1\n",
" 0\n",
" 0\n",
" 0\n",
" 0\n",
" 0\n",
" 0\n",
" 1\n",
" 0\n",
" 1\n",
" 0"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xTrain=array(xTrain)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"891x7 Array{Any,2}:\n",
" 3 22.0 \"male\" 1 0 7.25 \"S\"\n",
" 1 38.0 \"female\" 1 0 71.2833 \"C\"\n",
" 3 26.0 \"female\" 0 0 7.925 \"S\"\n",
" 1 35.0 \"female\" 1 0 53.1 \"S\"\n",
" 3 35.0 \"male\" 0 0 8.05 \"S\"\n",
" 3 29.6991 \"male\" 0 0 8.4583 \"Q\"\n",
" 1 54.0 \"male\" 0 0 51.8625 \"S\"\n",
" 3 2.0 \"male\" 3 1 21.075 \"S\"\n",
" 3 27.0 \"female\" 0 2 11.1333 \"S\"\n",
" 2 14.0 \"female\" 1 0 30.0708 \"C\"\n",
" 3 4.0 \"female\" 1 1 16.7 \"S\"\n",
" 1 58.0 \"female\" 0 0 26.55 \"S\"\n",
" 3 20.0 \"male\" 0 0 8.05 \"S\"\n",
" \u22ee \u22ee \n",
" 1 56.0 \"female\" 0 1 83.1583 \"C\"\n",
" 2 25.0 \"female\" 0 1 26.0 \"S\"\n",
" 3 33.0 \"male\" 0 0 7.8958 \"S\"\n",
" 3 22.0 \"female\" 0 0 10.5167 \"S\"\n",
" 2 28.0 \"male\" 0 0 10.5 \"S\"\n",
" 3 25.0 \"male\" 0 0 7.05 \"S\"\n",
" 3 39.0 \"female\" 0 5 29.125 \"Q\"\n",
" 2 27.0 \"male\" 0 0 13.0 \"S\"\n",
" 1 19.0 \"female\" 0 0 30.0 \"S\"\n",
" 3 29.6991 \"female\" 1 2 23.45 \"S\"\n",
" 1 26.0 \"male\" 0 0 30.0 \"C\"\n",
" 3 32.0 \"male\" 0 0 7.75 \"Q\""
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"accuracy = nfoldCV_forest(yTrain, xTrain, 5, 20, 4, 0.7)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Fold 1"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Classes: {0,1}\n",
"Matrix: \n",
"[125 12\n",
" 23 62]\n",
"Accuracy: 0.8423423423423423\n",
"Kappa: 0.6579804560260585\n",
"\n",
"Fold "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2\n",
"Classes: {0,1}\n",
"Matrix: \n",
"[134 11\n",
" 26 51]\n",
"Accuracy: 0.8333333333333334\n",
"Kappa: 0.6145471609572971\n",
"\n",
"Fold "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3\n",
"Classes: {0,1}\n",
"Matrix: \n",
"[121 19\n",
" 26 56]\n",
"Accuracy: 0.7972972972972973\n",
"Kappa: 0.5570630486831604\n",
"\n",
"Fold "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4\n",
"Classes: {0,1}\n",
"Matrix: \n",
"[110 15\n",
" 26 71]\n",
"Accuracy: 0.8153153153153153\n",
"Kappa: 0.6198312588756161\n",
"\n",
"Mean Accuracy: 0.822072072072072\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"4-element Array{Float64,1}:\n",
" 0.842342\n",
" 0.833333\n",
" 0.797297\n",
" 0.815315"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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